PROJECT SUMMARY/ABSTRACT Perturbation of cancer cells often leads to heterogeneous outcomes, in that most cells exhibit a dominant phenotype, but the rest appear resistant or hypersensitive to the perturbation. If the penetrance of such a phenotype is heritably incomplete, then it becomes extremely difficult to decipher the upstream molecular events that heterogenize the population and cause response variability. By combining quantitative measurements with dynamical models, systems approaches should be useful if provided with a core network of important biomolecules. The daunting hurdle lies in identifying phenotype-relevant regulatory heterogeneities that define the network for penetrance at the single-cell level. Our proposal seeks to exploit a new approach, called stochastic frequency matching (SFM), for elaborating the molecular networks upstream of incompletely penetrant phenotypes. SFM identifies and parameterizes single-cell heterogeneities?which emerge after a uniform perturbation but before the appearance of a variable phenotype?to hone in on regulatory states corresponding to future penetrance. For an onco-phenotype incompletely triggered by ErbB receptor tyrosine kinase signaling in 3D cultured breast epithelia, we implemented SFM using microarrays to uncover a network of critical nucleocytoplasmic regulators. The goals of this proposal are to apply systems approaches to the ErbB nucleocytoplasmic network and adapt SFM more broadly to RNA sequencing of breast cancer patients with ErbB amplification. Based on our provisional SFM results, we hypothesize that ErbB signaling heterogeneously reconfigures the nucleocytoplasmic shuttling state of cells to determine incomplete penetrance of the onco-phenotype. The aims are to: 1) Identify network-level mechanisms for the incompletely penetrant ErbB1:ErbB2 phenotype. 2) Determine whether drivers of incomplete penetrance in 3D define shuttling states in human cancers and promote ErbB2-driven mammary tumors in mice. 3) Sequence and parameterize regulatory-state heterogeneity in HER2+ breast cancers to assemble patient-specific network models of shuttling variability and sensitivity. Drivers of incomplete penetrance are important for understanding transitions during tumor initiation-progression and for developing therapeutic interventions with more reliable patient outcomes. SFM gives the Cancer Systems Biology Consortium a means to identify driver networks in a comprehensive and hypothesis-driven way.